contains an Editor allowing the user to prepare tables
directly, but tables may also be imported from a spreadsheet such as Excel®
or obtained through the Collector associated to Dynamic
Multivalued tables containing symbolic
values are converted into one-valued tables called contingency
In turn, one-valued tables may be converted into Burt's tables
(tables of cooccurrences). These tables are particularly useful to
study the dependence and clustering of attributes.
The most powerful technique offered
by SEMANA is
Correspondence Factor Analysis (CFA) coupled with Hierarchical
Ascending Classification (HAC) according to programs written by
J.-P. Benzecri and coworkers in the 1970s.
report gives the Eigenvalues (inertia of the axes), the projections of
each object and attribute onto the first 4 axes and the contribution of
each axis to the definition of each point and the contribution of each
point to the definition of the axes. Projections in planes [1,2] and
[1,3] are proposed by default, but any other plane may be represented.
The HAC is also displayed and classes may be colorized to help
For beginners in Statistical Data Analysis looking for a good
introduction to Factor Analysis, see http://www.micheloud.com/FXM/COR/e/index.htm
Other statistical tools are available in SEMANA:
K-Means : an algorithm to cluster a set of n
objects defined by attributes into k partitions (k < n being defined
by the user). see K-means
algorithm in Wikipedia.
: for each couple of attributes, a test of reduced deviation
("écart-réduit") is performed and expressed as a positive or negative
number between 0 and 100, according as the correlation is positive or
negative (+100 means a perfect correlation; 0 = independence; -100 =
perfect inverse correlation).
Matrix of distance
: The matrix of distance between objects or between attributes is
calculated according to various metrics : Euclidean distance, Chi2,
Jaccard, Sokal and Michener (or Hamming distance). The results are
displayed in cross tables and as ordered lists. The procedure applies
to one-valued tables.
Feature matching : A similarity index calculated
according to Tversky'
model is given for each pair of objects. The procedure applies to
multi-valued tables (see Zhao et al. 2006).
BENZECRI J.-P. (1984). L'analyse des données. Vol. 1 : La
Vol. 2 : L'Analyse des Correspondances.
Ed. Dunod, Paris, 4ème éd.
JAMBU M. (1978). Classification automatique pour l'analyse des
1 : Méthodes et algorithmes ; vol. 2 : Logiciels (avec M.-O.
Lebeaux). Ed. Dunod, Paris.
FENELON J.P. (1981). Qu'est-ce que l'analyse des données?
Yi Zhao, Xia Wang, Wolfgang Halang (2006). Ontology Mapping based on
Rough Formal Concept Analysis. Proceedings
of the Advanced International Conference on Telecommunications and
International Conference on Internet and Web Applications and Services
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